Modern machine learning models are achieving great successes in real world medical applications, such as diabetic retinopathy diagnosis [1]}, skin cancer classification [2]}, or lung disease assessment [3]}, [4]}, [5]}.
Due to the early and profound digitization of imaging techniques, machine learning in radiology can already show convincing successes, such as the detection of certain critical pathologies of the lung on X-ray images non-inferior to radiologists [3]}.
Considering the increasing demand for imaging while the number of radiologists remains insufficient, such and similar models can help improve medical patient care [7]}, [8]}, [9]}, [10]}, [11]} , e.g., by screening acquired radiographs for critical findings prior to radiologist interpretation.
Then, patients with time sensitive illnesses will receive treatment earlier, potentially saving their lives.
